A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas
Remote sensing of land surface temperature (LST) is a fundamental variable in analyzing temperature variability in urban areas. Geostationary sensors provide sufficient observations throughout the day for a diurnal analysis of temperature, however, lack the spatial resolution needed for highly heter...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/1/45 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548972959727616 |
|---|---|
| author | Alexandra Hurduc Sofia L. Ermida Carlos C. DaCamara |
| author_facet | Alexandra Hurduc Sofia L. Ermida Carlos C. DaCamara |
| author_sort | Alexandra Hurduc |
| collection | DOAJ |
| description | Remote sensing of land surface temperature (LST) is a fundamental variable in analyzing temperature variability in urban areas. Geostationary sensors provide sufficient observations throughout the day for a diurnal analysis of temperature, however, lack the spatial resolution needed for highly heterogeneous areas such as cities. Polar orbiting sensors have the advantage of a higher spatial resolution, enabling a better characterization of the surface while only providing one to two observations per day. This work aims at using a multi-layer perceptron-based method to downscale geostationary-derived LST based on a polar-orbit-derived one. The model is trained on a pixel-by-pixel basis, which reduces the complexity of the model while requiring fewer auxiliary data to characterize the surface conditions. Results show that the model is able to successfully downscale LST for the city of Madrid, from approximately 4.5 km to 750 m. Performance metrics between training and validation datasets show no overfitting. The model was applied to a different time period and compared to data derived from three additional sensors, which were not used in any stage of the training process, yielding a R<sup>2</sup> of 0.99, root mean square errors between 1.45 and 1.58 and mean absolute errors ranging from 1.07 to 1.15. The downscaled LST is shown to improve the representation of both the temporal variability and spatial heterogeneity of temperature, when compared to geostationary- and polar-orbit-derived LST individually. The resulting downscaled data take advantage of the high observation frequency of geostationary data, combined with the spatial resolution of polar orbiting sensors and may be of added value for the study of diurnal and seasonal patterns of LST in urban environments. |
| format | Article |
| id | doaj-art-1d48d54630854399bcc0335b2d6acfae |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-1d48d54630854399bcc0335b2d6acfae2025-01-10T13:20:03ZengMDPI AGRemote Sensing2072-42922024-12-011714510.3390/rs17010045A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban AreasAlexandra Hurduc0Sofia L. Ermida1Carlos C. DaCamara2Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, PortugalInstituto Português do Mar e da Atmosfera (IPMA), 1749-077 Lisbon, PortugalInstituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, PortugalRemote sensing of land surface temperature (LST) is a fundamental variable in analyzing temperature variability in urban areas. Geostationary sensors provide sufficient observations throughout the day for a diurnal analysis of temperature, however, lack the spatial resolution needed for highly heterogeneous areas such as cities. Polar orbiting sensors have the advantage of a higher spatial resolution, enabling a better characterization of the surface while only providing one to two observations per day. This work aims at using a multi-layer perceptron-based method to downscale geostationary-derived LST based on a polar-orbit-derived one. The model is trained on a pixel-by-pixel basis, which reduces the complexity of the model while requiring fewer auxiliary data to characterize the surface conditions. Results show that the model is able to successfully downscale LST for the city of Madrid, from approximately 4.5 km to 750 m. Performance metrics between training and validation datasets show no overfitting. The model was applied to a different time period and compared to data derived from three additional sensors, which were not used in any stage of the training process, yielding a R<sup>2</sup> of 0.99, root mean square errors between 1.45 and 1.58 and mean absolute errors ranging from 1.07 to 1.15. The downscaled LST is shown to improve the representation of both the temporal variability and spatial heterogeneity of temperature, when compared to geostationary- and polar-orbit-derived LST individually. The resulting downscaled data take advantage of the high observation frequency of geostationary data, combined with the spatial resolution of polar orbiting sensors and may be of added value for the study of diurnal and seasonal patterns of LST in urban environments.https://www.mdpi.com/2072-4292/17/1/45land surface temperaturegeostationarydownscalingurbanmulti-layer perceptron |
| spellingShingle | Alexandra Hurduc Sofia L. Ermida Carlos C. DaCamara A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas Remote Sensing land surface temperature geostationary downscaling urban multi-layer perceptron |
| title | A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas |
| title_full | A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas |
| title_fullStr | A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas |
| title_full_unstemmed | A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas |
| title_short | A Multi-Layer Perceptron Approach to Downscaling Geostationary Land Surface Temperature in Urban Areas |
| title_sort | multi layer perceptron approach to downscaling geostationary land surface temperature in urban areas |
| topic | land surface temperature geostationary downscaling urban multi-layer perceptron |
| url | https://www.mdpi.com/2072-4292/17/1/45 |
| work_keys_str_mv | AT alexandrahurduc amultilayerperceptronapproachtodownscalinggeostationarylandsurfacetemperatureinurbanareas AT sofialermida amultilayerperceptronapproachtodownscalinggeostationarylandsurfacetemperatureinurbanareas AT carloscdacamara amultilayerperceptronapproachtodownscalinggeostationarylandsurfacetemperatureinurbanareas AT alexandrahurduc multilayerperceptronapproachtodownscalinggeostationarylandsurfacetemperatureinurbanareas AT sofialermida multilayerperceptronapproachtodownscalinggeostationarylandsurfacetemperatureinurbanareas AT carloscdacamara multilayerperceptronapproachtodownscalinggeostationarylandsurfacetemperatureinurbanareas |